Unraveling a Secret: Vietnam's Outstanding Performance on the PISA test

Policy Research Working Paper 7630
Unraveling a Secret
Vietnam’s Outstanding Performance on the PISA Test
Suhas D. Parandekar
Elisabeth K. Sedmik
Education Global Practice Group
April 2016
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Abstract
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Policy Research Working Paper 7630
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This paper seeks to find an empirical explanation of Viet-
nam’s outstanding performance on the Programme for
International Student Assessment (PISA) in 2012. Only
a few developing countries participate in the assessment.
Those who do, with the unique exception of Vietnam, are
typically clustered at the lower end of the range of the
Programme for International student Assessment scores.
The paper compares Vietnam’s performance with that of
a set of seven developing countries from the 2012 assess-
ment’s data set, using a cut-off per capita GDP (in 2010
purchasing power parity dollars) of $10,000. The seven
developing countries’ average performance lags Vietnam’s
by more than 100 points. The “Vietnam effect” is difficult
to unscramble, but the paper is able to explain about half
of the gap between Vietnam and the seven countries. The
analysis reveals that Vietnamese students may be approach-
ing their studies with higher diligence and discipline, their
parents may have higher expectations, and the parents
may be following up with teachers regarding those expec-
tations. The teachers themselves may be working in a
more disciplined environment, with tabs being kept on
their own performance as teachers. Vietnam may also be
benefiting from investments in pre-school education and
in school infrastructure that are disproportionately higher
when compared with Vietnam’s per capita income level.

Unraveling a Secret: Vietnam’s Outstanding
Performance on the PISA Test
Suhas D. Parandekar
Elisabeth K. Sedmik
Global Practice for Education, The World Bank
Keywords: PISA; Vietnam; Oaxaca-Blinder Decomposition; Fryer-Levitt; Economics
of Education.
JEL Classiﬁcation Numbers: I21 (Analysis of Education); I28 (Government Policy);
Z18 (Public Policy).
This paper has been written using open source software: R for the econometric anal-
ysis and graphics and LaTeX for typesetting. Thanks to all who make free software
possible and to OECD for making the PISA data freely and easily available to anyone.
The R and Latex code used in writing this paper is freely available for download at
http://github.com/zagamog/PISA PAPER. The authors would like to thank World Bank
colleagues Amer Hasan, Marguerite Clarke, and Thanh Thi Mai for reading earlier versions
of the paper and providing helpful feedback. Errors and omissions are the responsibility of
the authors only.

1 Introduction
Vietnam participated in the Programme for International Student Assessment (PISA)
for the first time in 2012 and its performance has been much higher than other developing
countries that take part in this OECD led initiative. PISA scores of 15 year-olds in Mathe-
matics, Reading and Science are calibrated to an OECD mean of 500 and standard deviation
of 100 points. Only a few developing countries take part in PISA, perhaps because most of
them have results much lower than the OECD countries. In the OECD-PISA 2012 database,
there are seven countries other than Vietnam with a per capita GDP (in 2010 PPP dollars)
below US$ 10,000 - Albania, Colombia, Indonesia, Jordan, Peru, Thailand and Tunisia. At
US$ 4,098, Vietnam’s GDP per capita is the lowest of this group. Figure 1 indicates a posi-
tive, albeit non-linear correlation between GDP per capita and PISA test scores. Vietnam,
represented by a red star, lies much above the other developing countries clustered in the
lower left hand corner of Figure 1. With a mathematics mean score of 511, Vietnam is more
aligned to Finland (519) and Switzerland (531), rather than Peru (368) and Colombia (376).
Figure 1: PISA 2012 results compared with GDP per capita
0 10000 20000 30000 40000 50000 60000
300400500600700
GDP per Capita in PPP 2010
PISAMathAverageScore2012
Vietnam (511)
Colombia (376)
Peru (368)
Shanghai−China
Switzerland
(531)
Finland
(519)
Source: OECD-PISA database
The weighted average mathematics score of the seven developing countries is 383. It
is helpful to understand the significance of the 128 point difference of the seven countries
as compared with Vietnam. According to a recent OECD publication [OECD, 2013a], “an
entire proficiency level in mathematics spans about 70 score points –a large difference in the
2

skills and knowledge students at that level possess. Such a gap represents the equivalent of
about two years of schooling in the typical OECD country.” Applying this heuristic would
imply a nearly 3 year difference in educational attainment between Vietnam and the group of
seven developing countries in the PISA database. It should be noted at the outset that cross-
section data from one application of PISA does not permit causal inference, but correlations
can still provide useful insights. The difference is not only for mathematics and not just in
the mean score, but spanning the entire test distribution, as can be seen in Figure 2.
Figure 2: Kernel Density comparison between Vietnam and other Developing Countries
−200 0 200 400 600 800 1000
0.0000.0020.004
Science Score
Density
500
GROUP OF 7
Vietnam
OECD Average
(a) Science
0 200 400 600 800 1000
0.0000.0020.004
Mathematics Score
Density
500
GROUP OF 7
Vietnam
OECD Average
(b) Mathematics
−200 0 200 400 600 800 1000
0.0000.0020.004
Reading Score
Density
500
GROUP OF 7
Vietnam
OECD Average
(c) Reading
A range of alternative classifications are possible to organize the explanatory factors avail-
able in the OECD-PISA database. Figure 3 presents four sets of factors, starting clockwise
from the right. This is admittedly an arbitrary classification, utilized merely for expository
purposes as we consider each of the constituent variables in turn.
Figure 3: Conceptual Scheme based on available comparative variables
3

The approach of this paper is as follows. We begin in Section 2 by examining closely the
mean differences between Vietnam and the collective group of seven developing countries,
termed as “Dev7” for this paper (not to be confused with the G-7 of wealthy countries).
Comparing means in this context is a first pass at understanding the performance anomaly of
Vietnam on empirical grounds. Do Vietnamese 15 year olds somehow enjoy better cultural,
social or civic endowments to balance their economic disadvantages? An examination of
mean differences will provide us with a first set of tentative hypotheses.
The insights provided by mean differences need to be explored further by a regression
of the test scores on the explanatory variables. Large differences in means may not amount
to much if the associated variables are not correlated with test scores. In Section 3 we
adopt the regression methodology used by Fryer and Levitt to understand differences in
test score results of black children in the first two years of schooling in the United States
[Fryer and Levitt, 2004]. Fryer and Levitt are able to explain away all of a 0.62 standard
deviation negative achievement gap for black kindergarten children. In our case, we are able
to explain about half of a larger 1.28 standard deviaton positive achievement gap for Vietnam
compared to Dev7 countries. The lower ability of the Fryer-Levitt method to explain the
“Vietnam gap” is probably accounted for by the fact that per capita GDP lower than US
$ 10,000 is the only common support across diverse economic, political and educational
systems.
The Fryer-Levitt method deepens the understanding from mean comparisons, but what
it does not reveal may be as interesting as what it does. Our Fryer-Levitt adaption is
based on a pooled regression of eight developing countries, where we follow the fate of the
magnitude of the coefficient of the dummy variable representing the Vietnamese students
in the sample. However, we also need to investigate structural differences in the effects of
endowments between Vietnam and Dev7 countries. In Section 4, we adopt an approach first
used to explain variation in PISA performance between Germany and Finland by Andreas
Ammermueller [Ammermueller, 2007]. This is an adaptation of the popular Oaxaca-Blinder
decomposition of the wage earnings equation to uncover evidence of discrimination on the
basis of gender [Blinder, 1973] and [Oaxaca, 1973]. In this section, we examine closely the
structural differences between Vietnam and the Dev7 countries, including the contribution
of differences in endowments and the coefficients to the gap in test scores.
Even a multi-variate regression approach only proves correlation with nothing more than
a hint regarding causation, and so far we have only one year (2012) of PISA data for Vietnam.
Even though we cannot uncover causality, there are useful policy related conclusions that we
can derive from the analysis presented in this paper. There is a veritable industry of papers
4

regarding Finland’s PISA performance, directed mostly toward other OECD countries with
lower scores, for instance the United States. Vietnam’s superlative performance points to
a similar future stream of research, with the added advantage of relevance for developing
countries. Section 5 provides concluding ideas that might be among the first of many more
such ideas for future investigations of Vietnam’s performance.
2 Endowment Differences
Utilizing the categorization of explanatory factors presented in Figure 3, this section
analyzes mean differences in explanatory factors on students, parents, teachers and schools.
All variable means presented in the tables are statistically different at the 95% significance
level, unless otherwise noted in the footnotes and figures in parentheses represent standard
deviations. PISA documentation, especially the technical report - [OECD, 2014a] provides
rich definitions and explanations of the variables used. Appendix tables A2, A3 and A4 of
this paper accordingly provide references mapping the variables used in this paper and the
original PISA variable names.
2.1 Student Characteristics
Table 1 begins an exploration of differences in mean values between Vietnamese and
Dev7 student characteristics. The absence of differences is sometimes as important as the
presence of differences. Table 1 indicates no differences by age or gender of students. The
PRESCHOOL variable shows the first instance of a large statistically significant difference.
While 78.88% of Dev7 students reported attending pre-school, the number of students at-
tending pre-school from the Vietnam sample was 91.20% - a sizable difference that is both
statistically and economically significant. The relationship between pre-school and later
educational outcomes has been studied very closely over the years. Longitudinal impact
evaluation studies regarding the Perry Pre-school project and Head Start in the US are
among the most cited studies in the economics literature1
. We can also see from the num-
bers of REPEAT in Table 1 that PISA takers in Vietnam were three times less likely to have
repeated a grade in the past (6.79% compared to 19.15%).
1
For detailed meta-analysis, see [Barnett, 1995] and [Schweinhart et al, 2005]
5

Table 1: Student characteristics and family background
Dev7 countries Vietnam
Variable Description MS Valid N MS Valid N
Fixed characteristics
FEMALE Sex of student 0.5265 41394 0.5336 4882
(0.4993) (0.4989)
AGE Age of student 15.8211 41394 15.7692 4853
(0.2895) (0.2885)
Student’s prior history
PRESCHOOL Attended Preschool 0.7888 40114 0.912 4866
(0.4082) (0.2833)
REPEAT Grade repeating 0.1915 40343 0.0679 4860
(0.3935) (0.2516)
Truancy from School
ST08Q01 Times late 1.5131 40663 1.1872 4873
for school (0.7648) (0.4685)
ST09Q01 Days unexcused 1.2192 40650 1.0999 4875
absence (0.5276) (0.3527)
ST115Q01 Times skipped 1.2585 40632 1.0764 4880
classes (0.545) (0.3216)
Parental background and family wealth
HISEI Highest parental 40.4196 32814 26.6023 4860
occupational status (22.5168) (19.855)
MISCED Educational level 3.1193 40486 2.1744 4844
of mother (ISCED) (1.9853) (1.6059)
WEALTH Family wealth -1.4606 40821 -2.1343 4881
possessions (1.2267) (1.1656)
CULTPOS Cultural possessions -0.1424 39905 -0.2361 4809
(0.9678) (1.0173)
HEDRES Home educational -0.7427 40579 -1.0743 4874
resources (1.1473) (0.9364)
BOOK N Number of books 53.6393 39631 50.786 4841
in family home (94.5556) (75.4031)
Notes: The variables relate to the questionnaires administered to students in the general
(non-rotated) booklet. For a more detailed description of variables, please see Tables A2,
A3, A4 in the Appendix.The variable means of Dev7 and Vietnam are statistically different
at the 95% significance level, except FEMALE. Figures in parenthesis represent standard
deviations.
The findings regarding PRESCHOOL and REPEAT indicate the possible importance of
the trajectory of the student prior to high school. Repetition rates are difficult as comparative
indicators of system quality because of the variations across countries in curriculum and
standards, but REPEAT is another interesting variable to keep in mind as a possible clue
to the mystery of Vietnam’s PISA performance. As in some other East Asian cultures,
Vietnamese parents expect their children to study hard. Though Mark Twain, translated
into Vietnamese, is quite a best seller for young readers in Vietnam, truancy from school is
not perceived benevolently by parents.2
Table 1 indicates a consistently lower truancy rate
2
A cultural explanation is possibly quite important in explaining Vietnam’s anomalous PISA results,
though the PISA data set may only be able to measure the possible effects of culture rather than measuring
cultural differences. Literature from the World Values Survey, that does seek to measure cultural differences,
6

for the three variables used. The question refers to the past two complete weeks of school
and we can see that Vietnamese students are less likely to have been late for school, have
fewer days of unexcused absence and skip fewer classes.3
The final set of variables in Table 1 concerns parental background and wealth at the stu-
dents’ home, including cultural resources and books at home which may work to stimulate
cognitive development. The PISA database includes a number of indices to measure aspects
such as wealth. These indices are based on underlying data regarding occupations and pos-
sessions. The scaling of raw data to indices is described in detail in the PISA technical report
[OECD, 2014a]. For HISEI, which describes parental occupation status, the OECD mean
is 50 and the OECD standard deviation is 15. Table 1 shows that HISEI for Dev7 parents
stands at 40.42 and is thus much higher than 26.60 for Vietnamese parents. MISCED refers
to the International Standard Classification of Education (ISCED) developed by UNESCO.
Table 1 shows that the average level of mother’s education (MISCED) for Dev7 was just
over 3, meaning Upper Secondary education, while for Vietnam the mean was just over 2,
meaning Lower Secondary education. The WEALTH index is set for an OECD mean of
zero and standard deviation of 1. Dev 7 countries wealth level was -1.5 and Vietnam’s was
-2.1, which is consistent with the data regarding occupational classification and mother’s
education. These findings indicate the close correlation of these variables with GDP per
capita. Another interesting finding concerns the indices CULTPOS, cultural possessions and
HEDRES, educational resources at home which have an OECD mean 0 and a standard de-
viation 1, as well as BOOK N, the number of books in family home. CULTPOS includes
classical literature, books of poetry and works of art. HEDRES includes reference books and
books to help with school work as well as a study desk and “a quiet place to study”. These
three variables are also in line with per capita income - with the Dev7 mean being lower
than the OECD mean, and Vietnam being lower than the Dev7 mean. One explanation
regarding Vietnam’s PISA performance can probably be ruled out - it does not seem likely
that Vietnamese households spend a disproportionately higher amount of their income on
acquiring possessions such as books and other objects that would give their children an edge
in life.
indicates that Vietnam is a positive outlier on discipline and authority orientation[Dalton and Ong, 2005].
3
In the student’s questionnaire, there is a telling question - student’s have to agree or disagree on a four
point Likert scale to the statement “If I had different teachers, I would try harder at school.”. Converted
into an index, the mean for Vietnam at 0.363 is lower than that for Dev7 at 0.525. This suggests a tendency
in Vietnamese students for greater self-responsibility.
7

2.2 Student Effort
The phenomenon of primary and high school children taking extra classes to supple-
ment in-school instruction in Vietnam is well known, see [Ha and Harpham, 2005] and
[Dang, 2007]. Table 2 indicates that while Dev7 students spent roughly 4.7 hours in such
classes (total of OUTMATH, OUTLANG and OUTSCIE), the Vietnamese student spends
nearly 2 hours more for a total of 6.6 hours per week in such classes, with the difference
being highest for OUTMATH. Vietnamese students also spent about 1 additional hour per
week doing homework (total of ST57Q01 and ST57Q02) compared to Dev7 students. The
highest difference in this set of variables concerns the variable ST57Q04, which relates to
extra classes taught by a commercial company. While most of the schools in Vietnam are
public or government schools, it is interesting to note that students report nearly 5 hours
of commercially provided extra lessons, while the total for Dev7 countries is only about 2
hours per week. Collectively, these variables indicate that Vietnamese students spent about
16 hours per week studying outside of school, compared to 13 hours per week for Dev7
students.
Table 2: Student studying time out of school
Weekly out-of-school hours per subject
OUTMATH (r) weekly out-of-school 1.828 23603 3.1305 3227
lessons in math (2.1539) (2.3133)
OUTREAD (r) weekly out-of-school 1.2882 23531 1.4483 3223
lessons in ’test language’ (1.9623) (1.8837)
OUTSCIE (r) weekly out-of-school 1.5609 23298 2.0927 3205
lessons in science (2.0456) (2.1776)
Weekly out-of-school hours approach
ST57Q01 (r) Out-of-school time 5.0953 23696 5.8145 3164
homework (5.0319) (5.7196)
guided homework (2.9296) (3.2384)
personal tutor (2.7884) (2.938)
classes by company (3.3487) (4.8058)
parent/family member (3.055) (3.2442)
learn on computer (3.5519) (3.0496)
Notes: The variables relate to the questionnaires administered to students in the rotated book-
let, marked with (r). For a more detailed description of variables, please see Tables A2, A3,
A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically different at the
95% significance level. Figures in parenthesis represent standard deviations.
8

2.3 Student Attitudes
PISA applications in each test round have a focus on one of the subjects and in PISA 2012
the focus subject was mathematics. Mathematics happens to be the subject where the mean
score difference is highest between Vietnam and Dev7 countries. The PISA questionnaire
for students includes a very interesting series of questions regarding students’ perceptions of
their abilities, their effort and their reported practices. The details of these questions can
be found in the PISA technical report [OECD, 2014a]. Typically, each question includes
a set of Likert scaled items to which the student provides a discrete response on a four
point agree-disagree scale. These responses are then combined under specified algorithms
to provide an index value. For instance, MATWKETH, is meant to measure a student’s
“mathematics work ethic”. Students either agree or disagree with a set of 9 items on a 4
point likert scale - strongly disagree, disagree, agree and strongly disagree. The items include
items such as “I work hard on my mathematics homework”, and “I listen in mathematics
class”, “I keep my mathematics work well organized”. In the case of MATWKETH, when
a student agrees/strongly agrees with a positive statement, or disagrees/strongly disagrees
with a negative statement, he or she would tend to be deemed to have a stronger work
ethic towards mathematics. The raw data from the Likert scale is converted into an index
using IRT scaling procedures, so that the mean for OECD countries is 0 and the standard
deviation is 1. Table 3 indicates a most interesting finding regarding a range of such indices
from the PISA database.
Table 3: Student self-perception regarding mathematical ability and student effort
Indices susceptible to ’bragging’ tag
MATWKETH (r) Mathematics 0.4514 26140 -0.0014 3217
work ethic (0.9782) (0.6915)
SUBNORM (r) Subjective norms 0.716 26509 -0.0923 3220
in mathematics (1.165) (0.8395)
OPENPS (r) Openness to 0.1949 25612 -0.6125 3207
problem solving (0.9787) (0.8708)
SCMAT (r) Self-concept of 0.1673 26222 -0.1896 3249
own math skills (0.8101) (0.5903)
Indices less related to bragging/being boastful
PERSEV (r) Perseverance 0.3387 25710 0.4475 3211
in problem solving (0.9605) (0.8767)
ANXMAT (r) Mathematics 0.3995 26275 0.2115 3248
Anxiety (0.7724) (0.6354)
MATINTFC (r) Mathematics 0.092 24827 0.3285 3181
intentions (0.9837) (1.0964)
Notes: The variables relate to the questionnaires administered to students in the rotated
booklet, marked with (r). For a more detailed description of variables, please see Tables
A2, A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically dif-
ferent at the 95% significance level. Figures in parenthesis represent standard deviations.
9

The upper panel in Table 3 indicates a set of indices for which the scores of Vietnamese
students are lower than the scores of Dev7 students. For example, the score for MATWKETH
is 0.45 for Dev7 and 0 for Vietnam. The variable SUBNORM is supposed to measure
subjective norms regarding mathematics. This construct relates to a student’s perceptions
regarding how other people in the student’s life value mathematics. It includes items such
as “my friends enjoy taking mathematics tests” and “my parents believe it’s important for
me to study mathematics.” Presumably, when this measure is high, the student has a high
subjective norm for mathematics. Table 3 shows that the resulting mean for Dev7 countries
is 0.72 and the corresponding value for Vietnam is -0.09. The index SCMAT includes items
such as “I learn mathematics quickly” and “I have always believed that mathematics is one
of my best subjects”. Vietnamese students, who scored more than 1 standard deviation
above the Dev7 students on the PISA math test, scored half a standard deviation lower on
SCMAT. What is going on here?
This mini-mystery within the overall mystery of Vietnam’s PISA performance can pos-
sibly be resolved by looking at some further indices. The lower panel of Table 3 reports on
indices where the balance tips to the other side - these are indices where Vietnamese students
have a higher mean value than Dev7 students. These three indices bear close examination.
PERSEV consists of items that purport to capture perseverance with a task or a problem to
resolve; ANXMAT is a negative index (less is better) that deals with mathematics anxiety
(for example, an item included in this index states that “I get very nervous doing mathemat-
ics problems”); MATINTFC relates to future mathematics intention, including items such
as “I am planning on majoring in a subject in college that requires lots of mathematics”.
One possible explanation, as indicated in the heading of the Table 3 panels, is that
Vietnamese students are brought up in a culture that stresses the importance of modesty
and humility as a pathway to learning. They may find it difficult to say great things about
themselves, because of cultural norms against bragging or boasting. The lower panel in Table
3, on the other hand includes items that are less prone for an immodest interpretation. To
say that you are not afraid of mathematics may not be perceived as bragging. In this
context, the Vietnamese students are less anxious and more confident about the future role
of mathematics in their life.4
4
It will be straightforward to examine this hypothesis more closely by performing an IRT scaling of the
underlying items for the indices. We can then test for differences between Vietnam and the Dev7 countries in
values of the location parameters linking the items to the index. Systematic differences will tend to support
the hypothesis laid out here.
10

2.4 Mathematics Curriculum
In addition to beliefs and perceptions of students regarding mathematics in general, PISA
also seeks to closely investigate the issues related to the content of mathematics instructions.
PISA incorporates a very interesting approach to avoid or minimize the bragging or over-
claiming problem referred to in the previous sub-section. The index FAMCON is constructed
out of a response to a question about mathematical concepts for which students are asked
“How familiar are you with the following items?” The list of items includes items such
as ‘Linear Equation’, ‘Quadratic Function’ and ‘Cosine.’ The list of items also includes
three nonsensical items or pseudo-concepts that sound fancy: ‘Proper Number’,‘Subjunctive
Scaling’ and ‘Declarative Fraction’. These items are termed as “FOIL”, and are used as
trick items to calibrate the response for over-claiming on part of the students. The index
without correction is presented as FAMCON, and the index with correction is presented as
FAMCONC. It is quite fascinating that with FAMCON, the ”uncorrected” version, Dev7
students come out apparently better than Vietnam students, with a mean value of 0.26 as
compared to 0.12. Unfortunately, this also included familiarity with non-existent items like
‘subjunctive scaling’ - or bragging. With the corrected version, FAMCONC, the Vietnamese
students turn out to do much better, with a mean value of 0.43 as compared -0.54 for Dev7,
as can be seen in Table 4.
Table 4: Student reported experience in mathematics
FAMCON (r) Familiarity with 0.2559 26164 0.1225 3243
math concepts (1.1654) (0.6935)
FAMCONC (r) FAMCON corrected -0.5441 25832 0.4297 3231
with FOIL (0.8768) (0.9057)
EXAPPLM (r) Experience with 0.1111 26133 -0.2418 3243
applied math tasks (1.06) (0.7624)
EXPUREM (r) Experience with pure -0.1384 25973 0.1587 3244
math tasks (0.9809) (0.8076)
Notes: The variables relate to the questionnaires administered to students in the rotated
booklet, marked with (r). For a more detailed description of variables, please see Tables
A2, A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically dif-
ferent at the 95% signiﬁcance level. Figures in parenthesis represent standard deviations.
The index EXAPPLM asks students about their experience during school work with
examples of applied mathematics problems. Similarly, the index EXPUREM refers to expe-
rience with examples of pure mathematics. Not surprisingly, Vietnamese students indicate
a lower performance on EXAPPLM and a higher performance on EXPUREM.5
5
It has been a long standing issue that Vietnamese students are expected to learn a curriculum that is
more “crammed” than the international norm and contains more theory and abstract mathematics rather
than applied mathematics. See [Danh Nam Nguyen and Trung Tran, 2013] and [Tuan Anh Le, 2007].
11

2.5 Parental Support at School
The publication of the bestselling book [Chua, 2011] “Battle Hymn of the Tiger Mother”
in 2011 ignited a firestorm of controversy. The book gave prominence in popular culture to a
vast academic literature regarding parenting styles and the perceived higher performance of
children from Asian immigrant families in the US and other Western countries. One of the
ways that parents influence their children’s educational outcome is through the interaction
that parents have with their child’s teachers and others at school. The PISA data includes
a question that tries to examine parental expectations towards schools. The question SC24
includes a statement “There is constant pressure from many parents, who expect our school
to set very high academic standards and to have our students achieve them.”6
Table 5
indicates a higher level of PARPRESSURE (an index derived from SC24) for Vietnam,
compared to Dev7. Another question (SC25) asks school principals about the proportion of
parents that take part in a set of 12 activities. While the question does not specify which
parent (or both) may be involved, the variables, that may contain more than one of these
activities, have been named after the mother for ease of exposition.
Table 5: Parental Support at School
PARPRESSURE Parental achievement 0.2665 40372 0.3837 4866
pressure (0.4421) (0.4863)
TIGERMOM Parent initiates - 52.4472 41394 62.4183 4882
progress discussion (38.097) (41.3743)
DUTYMOM Teacher initiates - 66.9737 41394 68.5543 4882
progress discussion (36.727) (37.4796)
VOLUMOM Parent Participation - 35.2134 41394 38.3623 4882
Volunteering (38.8428) (39.9773)
TEACHMOM Parent Participation - 12.1764 41394 38.2821 4882
Teaching Assistance (23.4241) (41.5357)
FUNDMOM Parent Participation - 23.0784 41394 59.6022 4882
Fundraising (35.2134) (44.0376)
COUNCILMOM Parent Participation - 36.4546 41394 23.1174 4882
School government (37.2252) (36.4406)
Notes: The variables relate to the questionnaires administered to schools. For a more detailed
description of variables, please see Tables A2, A3, A4 in the Appendix. The variable means of
Dev7 and Vietnam are statistically different at the 95% significance level. Figures in parenthe-
sis represent standard deviations.
TIGERMOM refers to the reported proportion of parents who discussed their child’s
behavior or the child’s progress “on their own initiative”, to differentiate from cases where
parents might have done so following the initiative of the teacher, termed as DUTYMOM.
6
[Hsin and Xie, 2014] investigate in great detail data from a set of longitudinal surveys that cover thou-
sands of children over a long period of time starting from their early childhood through high school. As part
of the explanation of the superior performance of Asian immigrant children, the authors report that “Asian
students report greater parental expectations of academic success.”
12

Table 4 shows a slightly higher number on DUTYMOM for Vietnamese parents compared to
Dev7, but a greater difference, more than ten percentage points for TIGERMOM. VOLU-
MOM refers to parents volunteering in various non-academic activities, such as field trips
or carpentry and yard work. Vietnamese parents appear to have a slight advantage with
regard to VOLUMOM, yet a much higher one when considering TEACHMOM, which refers
to parents volunteering as assistants to the teacher - 38.28% compared to 12.18% for Dev7.
Vietnamese parents also appear to be much more active in fund raising, looking at FUND-
MOM, though they may have less formal influence through school committees.
2.6 Teacher Characteristics
Conventional measures regarding student-teacher ratios and teacher certification show
some advantage for Vietnam over Dev7 as shown in Table 6.
Table 6: Teacher characteristics and management
Teacher numbers and teacher management
PROPCERT Proportion of 0.6757 35130 0.7961 4586
certified teacher (0.4042) (0.3978)
SMRATIO Mathematics 188.1791 33985 120.9773 4777
teacher-student ratio (158.6256) (43.6092)
SC35Q02 Professional development 40.5068 39550 49.0086 4762
in math in last 3 months (40.8546) (45.1706)
STUDREL (r) Teacher student 0.3794 25870 0.0186 3253
relations (1.0178) (0.8883)
TCH INCENTV Teacher appraisal -0.0317 41394 0.2687 4882
linked to incentives (1.0301) (0.6336)
Quality assurance of mathematics teachers through . . .
TCH MENT Teacher mentoring 0.8566 40734 0.9859 4882
as quality assurance (0.3505) (0.1181)
TCM PEER Teacher peer review 0.7916 41095 0.8382 4882
of lectures, methods etc (0.4061) (0.3683)
TCM OBSER Principal or senior 0.8015 41170 0.9785 4882
staff observations (0.3989) (0.1451)
TCM INSPE Observation of classes 0.5882 41020 0.8664 4882
external inspector (0.4922) (0.3402)
Notes: The variables relate to the questionnaires administered to schools and students in the ro-
tated booklet, marked with (r). For a more detailed description of variables, please see Tables A2,
A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically different at the
95% significance level. Figures in parenthesis represent standard deviations.
The overall student-teacher ratio is not much different for Vietnam and Dev7 and stands
at roughly 20 students per teacher. However, there are more specialized mathematics teach-
ers per student in Vietnam, as shown by the values for SMRATIO (121 in Vietnam compared
to 188 for Dev7). There is a higher percentage of certified teachers in Vietnam and higher
13

reported professional development in mathematics (SC35Q02). A very interesting variable
from a policy point of view regards the incentives for teachers. School principals were asked
to what extent performance appraisal or other forms of feedback are related to incentives for
teachers in seven different forms, from salary and bonus to public recognition and greater
job responsibilities. The answers were to be given on a 4 point scale: ‘No change’, ‘A small
change’, ’A moderate change’ and ’A large change’. We converted the rating into a Rasch
index, scaled to an OECD mean of 0 and standard deviation of 1. The mean for Dev7 for this
index, TCH INCENTV was -0.03 for Dev7 and 0.27 for Vietnam, indicating greater presence
of teacher incentives in Vietnam. The final set of variables in Table 6 deal with the way that
quality assurance regarding teacher performance is carried out, with help of a mentor, peer,
supervisor or external inspector. These variables indicate a higher prevalence of oversight
for teachers in Vietnam, with the difference being greatest for external inspections (86.64%
in Vietnam compared to 58.82% in Dev7 countries).
2.7 Pedagogical Practices
Pedagogical practices are an outcome of a complex interaction between curriculum and
related educational policies, economic possibilities and the cultural and historical context. It
is difficult to trace differences in these practices in a quantitative survey.7
Table 7 presents a
few variables that seek to capture variation in pedagogical practices. They indicate the higher
prevalence of national policies in Vietnam regarding the use of computers in the classroom
and the use of a standardized curriculum that specifies what has to be taught each month.
There is no difference with regard to the use of a single textbook. There is some difference in
the use of formative student assessment, with slightly higher percentage of use of assessments
to monitor teachers and schools in Vietnam. COGACT represents an OECD-PISA index
variable based on response to student reports regarding classroom practices such as teachers
requiring students to reflect on a problem or develop new procedures rather than rely on
common practices. This variable shows a much lower level of cognitive activation in Vietnam
(-0.33) compared to 0.30 for Dev7. In the final set of classroom management variables, an
interesting variation can be seen in DISCLIMA, an index variable that measures disciplinary
climate in class, and is higher for Vietnam (0.38) than Dev7 (-0.02).
7
For an interesting recent qualitative study that seeks to emulate the TIMSS video study for Vietnam,
see [Vu Dinh Phuong, 2014].
14

Table 7: Pedagogical practices
Policies applied
COMP USE Math policy - use of 0.4345 40800 0.6447 4815
computers in class (0.4957) (0.4787)
TXT BOOK Math policy - 0.7905 40557 0.7855 4882
same textbook (0.4069) (0.4105)
STD CUR Maths policy - 0.8705 40595 0.949 4882
standardized curriculum (0.3358) (0.22)
Fromative assessment used to . . .
ASS SCH monitor the schools 0.9111 40555 0.9799 4882
yearly progress (0.2846) (0.1403)
ASS TCH make judgements on 0.7764 40400 0.9912 4882
teachers’ effectiveness (0.4166) (0.0934)
Cognitive Activation in Mathematics
COGACT (r) Cognitive activation in 0.2998 26217 -0.3278 3249
mathematics lessons (0.975) (0.6647)
Classroom Management
STU FEEDB Seeking written feed- 0.7105 40788 0.8419 4882
back from students (0.4536) (0.3649)
CLSMAN (r) Teacher classroom 0.2394 25753 0.2163 3252
management (in math) (0.905) (0.7761)
DISCLIMA (r) Disciplinary climate -0.0243 26242 0.3747 3254
in class (mathematics) (0.9055) (0.6926)
Notes: The variables relate to the questionnaires administered to schools and students in the
rotated booklet, marked with (r). For a more detailed description of variables, please see Ta-
bles A2, A3, A4 in the Appendix.The variable means of Dev7 and Vietnam are statistically
different at the 95% significance level, except TXT BOOK. Figures in parenthesis represent
standard deviations.
2.8 School Characteristics
Table 8 indicates interesting basic differences between Vietnam and Dev7 school char-
acteristics. Vietnamese schools are about half as likely to be private schools (8% compared
to 17%) and less dependent on funding from student fees; in Vietnam, student fees account
for 17% of the school’s financing, compared to 26% on average for Dev7. One very useful
comparison comes from a question regarding the geographic location of the high school. The
percentage of schools reported in a VILLAGE (defined in PISA by population below 3,000
inhabitants), was 46% of high schools in Vietnam compared to 14% of High schools in Dev7
countries. With CITY, defined by a population above 100,000 inhabitants, we find only 23%
Vietnamese schools in cities, compared to 41% of high schools located in cities for Dev7
countries.
15

Table 8: School characteristics
PRIVATESCL Private school 0.1714 41182 0.0832 4882
dummy variable (0.3768) (0.2762)
SC02Q02 Funding for school 25.7233 34621 16.6104 4848
from student fees (36.0117) (26.3564)
VILLAGE School located 0.1403 41347 0.4584 4882
in a village (0.3473) (0.4983)
TOWN School located 0.4508 41347 0.3101 4882
in a town (0.4976) (0.4626)
CITY School located 0.4089 41347 0.2315 4882
in a city (0.4916) (0.4218)
CLSIZE Average class size 35.013 40771 42.5043 4882
(9.764) (8.7236)
SCHSIZE Number of enrolled 1057.0332 35062 1302.9009 4882
students at school (924.2422) (648.6821)
PCGIRLS Proportion of 0.4900 36342 0.5282 4882
girls at school (0.2597) (0.0801)
Notes: The variables relate to the questionnaires administered to schools. For a more de-
tailed description of variables, please see Tables A2, A3, A4 in the Appendix.The variable
means of Dev7 and Vietnam are statistically different at the 95% significance level. Figures
in parenthesis represent standard deviations.
The average class size in Vietnam is higher, with 43 students compared to 35 students
in Dev7 countries, and the schools in Vietnam are bigger, with average enrollment of 1,303
students compared to 1,057 in Dev7. There is also a slightly higher percentage of girls in
Vietnamese schools.
2.9 School Resources
The comparison of Vietnam and Dev7 regarding school resources may be showing that
Vietnam makes a deeper effective investment in education (Table 9). Schools in Vietnam have
a lower number of computers per student (0.22) compared to a Dev7 (0.39). However, the
ratio of computers connected to the Internet is slightly higher in Vietnam (78% compared to
76%). Indices on quality of school educational resources (SCMATEDU) show Vietnam with
-0.4941 value and Dev7 with -0.8145 value, and similar higher Vietnam level exists for quality
of physical infrastructure at the school (SCMATBUI). There is also a higher proportion of
schools that offer additional math classes. These differences indicate that Vietnam has made
it a priority to invest in Basic Education that compensates to some extent for its income
disadvantage compared to the Dev7. With regard to extra-curricular activities; there is a
mixed picture. Not all extra-curricular activities are shown in Table 9, but some indicate
lower prevalence in Vietnam compared to Dev7 - for instance school band and math club
(not shown, with similar pattern are chess club, IT club, art club). Some activities have
higher prevalence in Vietnam - school play/musical, mathematics competition, and sports
16

(not shown here). It would appear that even for extra-curricular activities, the prevalence of
activities that require greater effort or competition are more prevalent in Vietnam compared
to Dev7.
Table 9: School resources and Management
Resource quantity and quality
RATCMP15 Available computers 0.3909 39490 0.2216 4875
for 15-year-olds (0.5476) (0.3411)
COMPWEB Ratio of computers 0.7556 37446 0.7795 3634
connected to Internet (0.3578) (0.3109)
SCMATEDU Quality of school -0.8145 41373 -0.4941 4882
educational resources (1.1538) (0.9718)
SCMATBUI Quality of -0.6322 41221 -0.3988 4882
physical infrastructure (1.1113) (1.0161)
SCL EXTR CL School offers 0.6538 40869 0.9584 4882
additional math classes (0.4757) (0.1997)
Extra-curriculars
EXC1 BAND School offers 0.4710 40044 0.1678 4882
Band, orchestra or choir (0.4992) (0.3737)
EXC2 PLAY School offers 0.5928 40122 0.8509 4882
school play/musical (0.4913) (0.3562)
EXC5 MCLUB School offers 0.453 40154 0.2687 4882
mathematics club (0.4978) (0.4434)
EXC6 MATHCOMP School offers 0.6268 40215 0.8032 4882
Mathematics competition (0.4837) (0.3977)
EXC10 SPORT School offers 0.9321 40581 0.992 4882
sporting activities (0.2516) (0.089)
Leadership accountability and autonomy
SCORE PUBLIC Achievement data 0.345 40965 0.7567 4882
posted publicly (0.4754) (0.4291)
SCORE AUTHRITS Achievement data 0.8003 41139 0.8282 4778
tracked by authority (0.3998) (0.3773)
SCHAUTON School Autonomy -0.2542 41394 -1.0419 4882
in admin. decisions (1.1328) (0.9378)
TCHPARTI Teacher participation -0.2169 41394 -1.6445 4882
in admin. decisions (1.4457) (0.5188)
LEADCOM Communicating and acting 0.2387 41252 0.0894 4882
on defined school goals (1.1105) (0.6744)
STUDCLIM Student-related aspects 0.0485 40973 0.0418 4874
of school climate (1.1642) (0.6849)
TEACCLIM Teacher-related aspects -0.1997 40973 -0.0873 4874
of school climate (1.1474) (0.7125)
Notes: The variables relate to the questionnaires administered to schools. For a more detailed de-
scription of variables, please see Tables A2, A3, A4 in the Appendix.The variable means of Dev7 and
Vietnam are statistically different at the 95% significance level, except STUDCLIM. Figures in paren-
thesis represent standard deviations.
With regard to school leadership and autonomy, there appears to be less autonomy
and more accountability in Vietnam. The index variable SCHAUTON indicates a Dev7
mean value of -0.2542, higher than the Vietnam mean value of -1.0419 (recall that indices
are set to OECD mean of zero). Teachers in Vietnam have lower chances to participate
17

in school management - TCHPARTI indicates a Dev7 mean value of -0.2169 compared to
1.6445 for Vietnam. Principals in Dev7 are more likely to say that they communicate and
act on school goals (LEADCOM), but there is much higher prevalence of public posting of
school achievement data (SCORE PUBLIC) in Vietnam. Interestingly, even Dev7 countries
have high levels of achievement tracking data by authorities (80% of schools report this -
SCORE AUTHRITS). Finally with regard to the school climate, indices described further
in the PISA documentation, STUDCLIM (student climate) is roughly even between Viet-
nam and Dev7, but TEACCLIM (teacher climate), that includes variables such as teacher
absenteeism and teacher expectations of students, is higher for Vietnam.
2.10 Preliminary conclusions from comparison of endowments
In summary, the mean comparisons between Vietnam and Dev7 students finds a number
of potentially insightful results. Consider the four-fold classification of factors presented in
the conceptual diagram of Figure 3 - students, parents, teachers and the school, the findings
are summarized below.
Students: Students in Vietnam are more likely to have attended pre-school and less likely
to have repeated grades in the past. They are likely to behave more disciplined at school, skip
fewer classes, and assume greater responsibility for their own learning. Vietnamese students
are less likely to brag about their abilities and experience and yet work harder, especially out
of school, in extra classes. They tend to have lower anxiety about mathematics and higher
confidence about the usefulness of mathematics in their future.
Parents: Parents in Vietnam are likely to be more involved in the school life of their
children than parents of students in Dev7 countries. Though time spent on homework help
is similar in both groups, Vietnamese parents are more likely to volunteer and take part
in fund-raising for the school and help the teachers as classroom assistants. Vietnamese
parents are also more likely to seek to meet the teacher to discuss their child’s progress or
the child’s behavior on their own initiative. Principals in Vietnam report higher levels of
parental pressure.
Teachers: Teachers have similar levels of formal education in both groups, but Viet-
namese teachers may have had more recent professional development activities. There are
more specialist mathematics teachers at high schools in Vietnam, and teachers overall are
also more likely to be certified. The performance of teachers is more likely to be monitored
in Vietnam, with higher emphasis on student achievement and on making information about
that achievement public. Teachers also tend to have lower autonomy, more likely to be sub-
18

ject to centralized policies and work in an environment with higher prevalence of incentives
for performance. Principals report fewer problems with regard to teacher absenteeism, which
squares with an explanation about a Confucian heritage culture.
Schools: Vietnam has a much lower level of economic development compared to the
Dev7 countries, which is reflected in lower levels of educational attainment of parents and
lower level of home possessions, including so called cultural possessions such as artwork
and books. Also, comparatively more Vietnamese students go to school in villages and
small towns, reflecting the national population distribution. Yet, two things are striking
about schools - although schools have fewer computers compared to Dev7 countries, these
computers are as likely as Dev7 countries to be connected to the internet. Also, indices
regarding quality of school infrastructure and school educational resources are less deficient
in Vietnam compared to Dev7, which is indicative of substantive investments in schools in
the past few decades.
Overall, across these four domains of information, it seems likely that the PISA data
set is able to detect significant cultural differences between Vietnam and Dev7 countries.
There appears to be some influence of policy, looking at student achievement assessment
and teacher incentives, and higher levels of centralized controls, but the effectiveness of
such policies is also likely tied to cultural factors. Unlike the ‘World Values Survey’ the
set of PISA instruments is not suited to clearly identify cultural differences, for instance
through responses regarding beliefs, attitudes and practices defined specifically to discrimi-
nate between cultures. While mean differences provide interesting hints, they are essentially
bi-variate correlations. In order to tell us more about the correlations, which ones are more
important than others, and whether indeed some unobservable ‘Vietnamese culture’ variable
may be a plausible explanation, we need to unravel the mystery further through a study of
multi-variate correlations. We do this first by using the Fryer-Levitt approach.
3 Regression Approach I: Fryer-Levitt
We are now ready to investigate the secret a bit further by deepening our analytical
approach beyond a mere comparison of means. We adopt a simple methodology that is
easy to understand and interpret. Our approach closely follows [Fryer and Levitt, 2004]
who sought to explain the black-white achievement gap in the first two years of schooling
for children in the United States. For the results presented in this section, we pool the
student level data from Vietnam and Dev7 countries. Recall that Dev7 stands for the seven
developing countries in the 2012 PISA dataset with a per capita income below the cut-off
19

of US$10,000. The reason for focusing on developing countries is that we want to have a
common support with regard to a country’s wealth. If a rich country shows outstanding
results, perhaps it may be of interest to other rich countries which do not do as well, but
it is hardly of great interest to a poor country. But if a poor country does very well, and
stands out from the pack of poor countries that mostly do poorly in PISA, readers from
poor countries want to know what can explain such a phenomenon, since it clearly cannot
be attributed to the wealth of the country (as captured albeit imperfectly by per capita
GDP). We start by looking at the Mathematics scores, with the identical approach being
used for the other two PISA disciplines; Reading and Science.
3.1 Mathematics
We estimate a weighted least squares regression of student level test scores as follows:8
TESTSCOREi = V IETNAMiγ + XiΘ + i (1)
A key estimate of interest is γ, the coefficient on V IETNAM, a 0 or 1 dummy variable.
Regressions are run in a sequence, starting from one without any covariates in X, and then
adding variables in groups to expand X in consecutive columns in Table 10. Column (1) in
Table 10 shows that the Vietnam dummy has a coefficient of 128.05, when no other covariates
are added. By construction, this is the absolute difference in means between Vietnam and
the Dev7 countries. Next, we want to see the extent to which observable variables included
in the PISA dataset can help to explain this large gap of 128.05.9
The first set of variables
included in the regression reported in column (2) concern the students themselves. The
student characteristics were - if students went to pre-school, repeated a grade in the past,
and how often they are late for school (ST08Q01) or skipped classes (ST115Q01). With
these variables included, the coefficient on the dummy or “the Vietnamese advantage” or
“gap”, comes down by nearly 20 points, or roughly 0.2 standard deviation units, to 108.91.
In other words, one key reason that the Vietnam gap is so high is because of these student
related variables - this result was hinted at in the endowment comparison presented earlier
in Section 2. Note that of the four student variables used in column (2), only two are
8
This is a simplification, used to present our main idea. In PISA, the test score is not provided as a
single value but as a set of five plausible values for each student, and complex algorithms have to be used
for weighting based on a method called Balanced Repeated Replication (BRR) using Fay’s variant. Details
are provided in the PISA technical manual [OECD, 2014a]. In this paper, we utilize the R intsvy package
for implementation.
9
For explanatory variables not discussed in the previous sections but used for the regressions here, please
see Appendix Table A1 for a comparison of mean values.
20

statistically significant. Figures in parenthesis represent t-values.
As we add variables to expand the group of covariates for Mathematics (Table 10),
Reading (Table 11) and Science (Table 12) we follow a trial and error method, depending
on whether or not, within each group of variables (students, parents, teachers and schools),
the inclusion of the specific variable leads to a reduction in the Vietnam dummy coefficient.
We retain the variable if it leads to a reduction in the Vietnam dummy coefficient, even
if the variable itself may or may not be statistically significant. In this approach, we are
not interested in accurately capturing the size of the coefficients other than the one for
the Vietnam dummy. Neither are we seeking to maximize the explanatory power of the
regression. 10
After considering student related variables, the second set of variables relates to the home
background and parents of students. Mean comparisons showed that PISA indices on family
wealth or parental education are much lower for Vietnam compared to the other seven
countries. Clearly, Vietnam’s higher PISA scores cannot be explained by higher parental
wealth or prental education. Inclusion of these variables would increase the coefficient on
the Vietnam dummy and would take us away from our objective. If Vietnam had enjoyed
the Dev7 levels of those variables, the Vietnam gap would have turned to be larger than
128 points. Hence, the parent variables that are retained and presented in column (3) are
only those variables that reduce the Vietnam dummy. The reduction amounts to only 11
points compared to the nearly 20 point reduction for student variables. Only the variable
PARPRESSURE is statistically significant in this group and indeed has a sizeable impact
on the score. This variable reports on principals claiming that “there is constant pressure
from many parents, who expect our school to set very high academic standards and to have
our students achieve them.”
10
The R code for all statistical analysis undertaken in this research paper is freely available for download
at http://github.com/zagamog/PISA PAPER
21

Table 10: The estimated impact of ‘Vietnam’ on Mathematics PISA test scores
Mathematics
Variables (1) (2) (3) (4) (5)
VIETNAM 128.05 (5.65) 108.91 (5.32) 97.46 (5.48) 95.13 (5.87) 77.26 (7.84)
PRESCHOOL - - 45.86 (3.92) 40.54 (3.95) 39.21 (4.09) 24.90 (3.80)
REPEAT - - -50.57 (2.59) -47.55 (2.56) -45.05 (3.19) -36.96 (3.00)
ST08Q01 - - -8.59 (1.20) -8.41 (1.18) -8.38 (1.33) -7.84 (1.32)
ST115Q01 - - -4.94 (1.70) -4.57 (1.73) -6.10 (1.80) -5.40 (1.86)
BOOK N - - - - 0.09 (0.01) 0.08 (0.01) 0.07 (0.01)
PARPRESSURE - - - - 10.73 (5.01) 12.51 (4.78) 10.02 (4.40)
FUNDMOM - - - - 0.27 (0.06) 0.24 (0.06) 0.19 (0.07)
COUNCILMOM - - - - -0.14 (0.06) -0.18 (0.06) -0.10 (0.07)
DUTYMOM - - - - -0.07 (0.06) -0.12 (0.07) -0.10 (0.07)
PROPCERT - - - - - - 16.08 (5.50) 16.32 (6.87)
SMRATIO - - - - - - -0.01 (0.01) -0.03 (0.01)
TCSHORT - - - - - - -1.91 (1.97) 2.24 (1.87)
TCFOCST - - - - - - 0.30 (2.19) -1.45 (1.88)
TCM STUASS - - - - - - 10.85 (7.45) -0.18 (7.85)
TCM PEER - - - - - - -1.53 (6.59) -5.61 (5.65)
TCH INCENTV - - - - - - -0.92 (2.73) -2.75 (2.72)
ASS PROG - - - - - - -0.51 (15.51) -22.58 (8.04)
ASS PROM - - - - - - 7.60 (6.11) 14.09 (5.80)
ASS SCH - - - - - - 5.51 (6.51) 0.51 (7.31)
STU FEEDB - - - - - - 3.66 (5.27) 2.20 (5.07)
PCGIRLS - - - - - - - - 14.59 (13.65)
COMP USE - - - - - - - - -1.57 (5.30)
TXT BOOK - - - - - - - - -9.51 (7.05)
TOWN - - - - - - - - -9.53 (3.76)
CLSIZE - - - - - - - - 0.81 (0.23)
COMPWEB - - - - - - - - 15.28 (6.31)
SCMATEDU - - - - - - - - 5.58 (2.94)
SCMATBUI - - - - - - - - 3.46 (2.45)
EXC2 PLAY - - - - - - - - 8.69 (3.96)
EXC6 MATHCOMP - - - - - - - - -1.70 (5.37)
EXC10 SPORT - - - - - - - - -5.65 (9.15)
EXC11 UNICORN - - - - - - - - 6.81 (5.59)
SCL EXTR CL - - - - - - - - 10.90 (5.08)
SCORE PUBLIC - - - - - - - - 10.10 (4.75)
QUAL RECORD - - - - - - - - 6.99 (6.77)
SCHSEL - - - - - - - - 1.57 (3.29)
R2 27.21 37.24 40.03 43.15 43.93
N 48483 46267 44046 30051 25612
Notes: Figures in parentheses are t-values (95% significance level). For a more detailed description of variables,
please see Tables A2, A3, A4 in the Appendix.
The third set of variables, related to teachers is presented in column (4) and results in a
reduction of the Vietnam dummy by only 3 points to a level of 95.13. This does not mean that
teachers are unimportant as a reason for Vietnam’s superior performance in mathematics,
only that the observed teacher related variables in the PISA dataset do not collectively help
as an explanation. In the regression itself, one can see that PROPCERT, proportion of
certified teachers affects mathematics scores positively. The same goes for a few of student
assessment related variables, including TCM STUASS and STU FEEDB, that relate to the
use of student assessment and student written feedback to assess teachers.
22

The final set of variables, related to schools is presented in column (5), that results in
a further reduction of the dummy coefficient by 12 points, to a level of 77.26. There are
interesting insights from some of these school variables. COMP USE does not have a positive
effect on the mathematics score, but COMPWEB does - recall that Vietnam does relatively
better on internet connectivity compared to mere availability of computers. The presence
of SCMATEDU and SCMATBUI, quality of school educational resources and quality of
physical infrastructure, helps explain the gap, recall from Table 9 that Vietnam has superior
endowments compared to the Dev7 countries. Table 10 shows that extra classes organized
by the school (SCL EXTR CL) and the “systematic recording of data including teacher
and student attendance and graduation rates, test results and professional development of
teachers” (QUAL RECORD) are also part of the story of explaining the Vietnam test score
gap. Public dissemination by the school of test results (SCORE PUBLIC) has a positive
coefficient, which is also one of the variables where Vietnam appears to have twice as many
schools following this practice.
As we successively add variables in the regression equation, the number of observations in
the regressions drops significantly due to missing values in the data set. Comparing available
observable values to other variables for the dropped observations does not seem to indicate
a systematic bias in this attrition. The 2012 PISA round included a so called ‘rotated’
module for the student’s questionnaire. The idea of the rotated module was to ask three
additional sets of questions in a systematic division that increases the overall data available
without burdening the respondents with excessively long questionnaires. The main caveat
of the ‘rotated’ design is that these additional three sets of questions/variables only cover
two-thirds of students each, and any combination of two sets would only cover one-third
of students. Regressing on variables of the rotated modules, thus significantly reduces the
sample size. The regression tables with rotated variables included can be downloaded from
the github site for this paper. The lowest possible value for the Vietnam dummy in the
Mathematics regression was 49.59, adding gap-decreasing variables from the second rotated
module (appendix table A5 online). This relates to a 61% reduction of the Vietnam ‘gap’.
The rest of the gap is explained due to effects not measured by PISA.
23

3.2 Reading
Table 11: The estimated impact of ‘Vietnam’ on Reading PISA test scores
Reading
Variables (1) (2) (3) (4) (5)
VIETNAM 105.16 (5.03) 85.03 (4.33) 75.49 (4.69) 74.13 (4.59) 60.31 (6.06)
FEMALE - - 25.84 (1.69) 25.24 (1.74) 26.65 (1.87) 23.02 (1.60)
PRESCHOOL - - 41.47 (3.67) 37.82 (3.78) 34.86 (3.83) 23.68 (3.40)
REPEAT - - -58.46 (2.79) -55.65 (3.03) -52.78 (3.50) -43.16 (3.24)
ST08Q01 - - -8.6 (1.29) -8.31 (1.25) -8.03 (1.38) -7.67 (1.43)
ST115Q01 - - -7.9 (1.66) -7.82 (1.68) -9.63 (1.78) -8.37 (1.80)
BOOK N - - - - 0.08 (0.01) 0.06 (0.01) 0.05 (0.01)
PARPRESSURE - - - - 10.72 (4.32) 11.21 (4.24) 3.69 (4.18)
VOLUMOM - - - - -0.05 (0.06) -0.06 (0.05) -0.03 (0.06)
FUNDMOM - - - - 0.2 (0.06) 0.17 (0.05) 0.1 (0.06)
COUNCILMOM - - - - -0.12 (0.06) -0.16 (0.06) -0.09 (0.07)
DUTYMOM - - - - -0.05 (0.06) -0.08 (0.06) -0.1 (0.07)
PROPCERT - - - - - - 9.14 (4.84) 3.14 (5.59)
TCSHORT - - - - - - -3.93 (1.84) 0.47 (1.91)
TCM STUASS - - - - - - 15.91 (7.67) 7.35 (8.22)
ASS PROG - - - - - - 4.31 (16.68) -15.85 (10.44)
ASS PROM - - - - - - 6.25 (5.45) 13.94 (5.92)
ASS NAT - - - - - - 5.71 (5.41) 1.44 (5.91)
ASS CUR - - - - - - -2.15 (7.50) -3.81 (9.14)
STU FEEDB - - - - - - 4.95 (4.67) 6.12 (4.28)
PCGIRLS - - - - - - - - 23.11 (10.88)
TOWN - - - - - - - - -6.88 (3.71)
CLSIZE - - - - - - - - 0.95 (0.23)
COMPWEB - - - - - - - - 16.08 (5.56)
SCMATEDU - - - - - - - - 5.16 (2.52)
SCMATBUI - - - - - - - - 0.98 (2.35)
EXC2 PLAY - - - - - - - - 13.05 (3.92)
EXC6 MATHCOMP - - - - - - - - 7.65 (5.02)
EXC10 SPORT - - - - - - - - -4.43 (10.86)
EXC11 UNICORN - - - - - - - - 10.61 (5.11)
SCORE PUBLIC - - - - - - - - 5.24 (4.22)
LEADINST - - - - - - - - 1.54 (2.03)
QUAL RECORD - - - - - - - - -6.06 (7.10)
SCHSEL - - - - - - - - 1.39 (3.05)
TEACCLIM - - - - - - - - -1.3 (2.63)
R2 19.61 34.5 36.45 39.63 41.84
N 48483 46267 44046 35442 27331
Notes: Figures in parentheses are t-values (95% significance level). For a more detailed description of vari-
ables, please see Tables A2, A3, A4 in the Appendix.
For scores on Reading (table 11), the gap is lower to begin with: 105.16 compared to
128.05 for mathematics. The pattern revealed by the regession is quite similar: the stu-
dent specific variables bring down the Vietnam dummy coeffcient to 85; the parent related
variables accounts for a further 9 points, with only PARPRESSURE being statistically sig-
nificant.
24

3.3 Science
Table 12: The estimated impact of ‘Vietnam’ on Science PISA test scores
Science
Variables (1) (2) (3) (4) (5)
VIETNAM 134.56 (4.91) 115.45 (4.37) 103.85 (4.56) 101.83 (4.63) 88.54 (5.99)
FEMALE - - -2.21 (1.61) -3.08 (1.58) -1.95 (1.78) -4.93 (1.46)
PRESCHOOL - - 43.06 (3.42) 37.94 (3.60) 36.09 (3.65) 24.36 (3.57)
REPEAT - - -53.8 (2.63) -50.74 (2.86) -48.77 (3.26) -40.6 (3.24)
ST08Q01 - - -9.28 (1.11) -9.02 (1.16) -8.76 (1.28) -8.45 (1.26)
ST115Q01 - - -5.95 (1.68) -5.55 (1.72) -6.88 (1.81) -6.18 (1.89)
BOOK N - - - - 0.08 (0.01) 0.06 (0.01) 0.06 (0.01)
PARPRESSURE - - - - 5.87 (3.94) 6.42 (3.96) -0.27 (3.76)
FUNDMOM - - - - 0.25 (0.05) 0.22 (0.05) 0.17 (0.06)
COUNCILMOM - - - - -0.18 (0.05) -0.2 (0.05) -0.12 (0.06)
DUTYMOM - - - - -0.02 (0.05) -0.04 (0.06) -0.06 (0.07)
PROPCERT - - - - - - 11.67 (4.91) 5.35 (5.47)
TCSHORT - - - - - - -0.68 (1.82) 2.7 (1.83)
TCM STUASS - - - - - - 16.04 (6.86) 9.43 (7.39)
TCM PEER - - - - - - -2.65 (6.05) -5.68 (5.79)
ASS PROG - - - - - - -5.42 (16.65) -23.01 (9.17)
ASS PROM - - - - - - 4.23 (5.79) 12.41 (6.68)
ASS NAT - - - - - - 7.33 (4.71) 1.49 (4.93)
ASS SCH - - - - - - 2.63 (6.01) 0.66 (6.72)
ASS CUR - - - - - - -6.33 (7.54) -4.87 (8.73)
STU FEEDB - - - - - - 4.59 (4.48) 5.90 (4.68)
PCGIRLS - - - - - - - - 18.92 (11.11)
PRIVATESCL - - - - - - - - -1.03 (5.34)
TOWN - - - - - - - - -8.25 (3.16)
CLSIZE - - - - - - - - 0.83 (0.20)
COMPWEB - - - - - - - - 17.22 (5.61)
SCMATEDU - - - - - - - - 5.38 (1.75)
EXC2 PLAY - - - - - - - - 8.52 (3.34)
EXC6 MATHCOMP - - - - - - - - 1.5 (4.53)
EXC10 SPORT - - - - - - - - -1.73 (9.13)
EXC11 UNICORN - - - - - - - - 8.56 (5.04)
SCORE PUBLIC - - - - - - - - 11.28 (3.75)
LEADINST - - - - - - - - 1.57 (1.95)
QUAL RECORD - - - - - - - - -0.92 (5.55)
SCHSEL - - - - - - - - 1.52 (3.11)
TEACCLIM - - - - - - - - -1.82 (2.46)
R2 30.75 41.14 43.35 46.11 47.35
N 48483 46267 44046 35302 27224
Notes: Figures in parentheses are t-values (95% significance level). For a more detailed description of variables,
please see Tables A2, A3, A4 in the Appendix.
Teacher related variables for Reading only account for a further decline of 2 points, and
school variables reduced the dummy by 14 points to a level of 60.31 reported in column
(5) of Table 11. Appendix Table A6, available on the online github repository, reports the
regressions with rotated variables included which brings the dummy coefficient down to a
lowest level of 52.04. Finally we look at the Science results.
The gap between Vietnam and Dev7 is largest for science scores, with column (1) in Table
12 indicating the dummy coefficient at 134.56. The pattern of reduction in the dummy is
25

again similar to the previous two subjects - with the student specific variables accounting
for the biggest decline (19 points) , followed by school (13 points), parents (11.5 points) and
teacher related variables (2 points). The final specification reduces the dummy to 88.54.
Together with rotated variables, presented in Appendix Table A7, available on the online
github repository, the lowest value for the Vietnam dummy is 80.09, a total reduction of 55
points explained, but still leaving a large portion unattributable through the Fryer-Levitt
method.
3.4 Summarized insights from Fryer-Levitt
We can see that across the three test subjects, the Vietnam dummy comes down by
nearly 50 points, or half a standard deviation. We find that student, parent and school
related variables appear to explain part of Vietnam’s superior performance in PISA.
Students: The student related variables reflect two policy elements that could be useful
for other countries that seek to learn from Vietnam. The investment made by Vietnam
in pre-schools appears to have long lasting effects, and indeed in Vietnam the government
continues to invest deeply not only for universal pre-school, but also for early childhood care
services even prior to pre-school. A policy lesson can also be derived from the effect of class
repetition - cause and effect is difficult to extract in the case of repetition and test score
performance, but one can see that repetition is much lower in Vietnam (Table 1) and the
regression coefficient on a student being a repeater has a large negative value, even in the final
specification with all other variables included. The other student related variables regarding
being late for school and skipping classes perhaps do not have clear policy implications for
other countries but help us understand a cultural effect regarding Vietnam.
Parents: As noted in the text, the household wealth/possessions, parents’ education
levels and socio-economic indices reflect Vietnam’s per capita GDP and act against explana-
tions of the test score gap. Applying this trend, Vietnam would have benefited from a much
higher gap if it had been a wealthier country, ceteris paribus. There is an “advantage” that
Vietnamese children have in having more demanding parents, though perhaps Vietnamese
teenagers may not always see it that way. Parents are demanding not only of their children,
but apparently also of schools and generally parents appear to back up their demands by
contributing on their own as volunteers. Interestingly, even though the individual coefficients
of parent related variables are not statistically significant except for one variable, the vari-
ables appear to collectively influence the dummy coefficient up to one-tenth of a standard
deviation of test scores. As Amy Chua [Chua, 2011] attested, parental attitudes and behav-
26

iors are deeply influenced by cultural norms. There is a policy lesson here concerning the
freedom of access provided to parents to take part in the school life. Sometimes schools tend
to be insular places without much scope for parents to contribute, but measures to harness
parents’ contributions in their time as well as in cash and kind may yield positive results.
Teachers: Teachers are widely recognized to be the most important factor in many stud-
ies of student achievement. Yet, in this case, the inclusion of a number of teacher related
variables does not appear to be useful in explaining Vietnam’s achievement gap. It is in-
teresting to note that in the regressions, the variables individually tend to have statistically
significant coefficients, but do not affect the dummy coefficient. One teacher variable, the
proportion of certified teachers, is clearly economically and statistically significant, and it is
one where Vietnam has an advantage (80% vs. 68% certified). Variables that relate to the
use of student assessment in providing feedback to teachers on their performance are seen
to be important. The presence of other assessment and feedback related variables are also
in line with intuition. It is possible that the advantages which Vietnam enjoys with regard
to teachers are ‘swamped’ by the effects of variables for which Vietnam does not have an
advantage, so the net result is that the gap is not explained by PISA related teachers vari-
ables. It is also possible that the effect of teachers is particularly context specific, revealing
a weakness of the pooled regression approach of Fryer and Levitt. This last explanation is
further investigated in the next section of the paper.
School School resources matter with regard to PISA results in the international per-
spective, as the scatter plot in Figure 1 motivating this paper clearly shows. Developing
countries with the notable exception of Vietnam are clustered at the bottom left hand side.
And there is a positive slope with high scoring countries tending to be on the higher income
side. In this section, we see that the effort made by the Vietnamese government to invest in
education plays an important part in explaining the achievement gap. Even though Vietnam
may be poor with regard to per capita income, it is not as poor with regard to the quality of
educational resources and the quality of physical infrastructure. This can be seen in Figure
4 comparing the average PISA 2012 mathematics scores with a school infrastructure quality
index. Compared to Figure 1, where Vietnam is the country with the lowest GDP per capita
in the PISA 2012 data set and thus placed in the left hand side of the figure, Vietnam moves
more towards the middle in Figure 4. Using SCMATBUI (quality of school infrastructure),
Vietnam jumps ahead 11 places, with a similar story (not shown) regarding SCMATEDU
(quality of educational materials). A key reason is the investments by the Vietnamese gov-
ernment in schools in smaller towns and rural areas as classified by PISA, given that the
dispersion of school infrastructure is lower in Vietnam compared to other countries.
27

Figure 4: PISA 2012 results compared with School Infrastructure Quality
−1.5 −1.0 −0.5 0.0 0.5
300400500600700
School Infrastructure Quality Index
PISAMathAverageScore2012
Vietnam
Source:OECD-PISA database
4 Regression Approach II: Oaxaca-Blinder Decompo-
sition
A key weakness of the Fryer-Levitt approach is that the pooled regression does not allow
for regression coefficients to be different across countries. In this section, we set aside the
Fryer-Levitt method to look at the data from a different analytical perspective. We use the
Oaxaca-Blinder decomposition (OB) method, that has recently become quite popular for
PISA analysis after the initial work by [Ammermueller, 2007] comparing the PISA results
of Finland and Germany.
4.1 Overview of the OB Decomposition
The objective of OB is to decompose the mean differences. In this case it is the mean
difference in Vietnam’s PISA performance with each of the DEV7 countries individually.
Extensions of OB allow for decompositions to be made throughout the distribution rather
than only at the mean values, but we leave such an extension for further research regarding
Vietnam’s PISA performance. OB is based on a simple algebraic rearrangement of terms
of the OLS regression of test scores. The mean outcome difference to be explained (∆¯Y ) is
simply the difference of the mean outcomes for Vietnam and the comparison country. Let
us denote the scores as ¯YV and ¯YO, respectively:
28

∆¯Y = ¯YV − ¯YO (2)
Now, as the OLS error terms are of mean zero by construction, (2) can be represented
by
∆¯Y = ¯XV
ˆβV − ¯XO
ˆβO (3)
In the twofold version of OB that we use in this paper, (3) can be represented either as
∆¯Y = ( ¯XV − ¯XO) ˆβV
endowments
+ ¯XO( ˆβV − ˆβO)
coefficients
(4)
or as
∆¯Y = ( ¯XV − ¯XO) ˆβO
endowments
+ ¯XV ( ˆβO − ˆβV )
coefficients
(5)
depending on which country is used as a reference country. We focus on this paper on the
approach of (4), using Vietnam as the reference, and leave (5) and additional OB variations
to subsequent research.
We base the choice of regression specification on the findings so far. In line with per
capita GDP of Vietnam compared with the Dev7 variables, there are a series of income level
or wealth related variables for which Vietnam does poorly in comparison with Dev7. We
term these variables WEALTH related variables. We include in it all variables for which
Vietnam has poorer endowments, and the term WEALTH denotes that they have higher
mean values in Dev7 countries, which are wealthier than Vietnam. These variables have
typically not been included in the Fryer-Levitt regressions as they would have exacerbated
rather than reduced the Vietnam gap. For example, the set includes mother’s highest level of
education, or MISCED, which is one ISCED level higher for Dev 7 as compared to Vietnam.
A second set of variables, which we term as ED WEALTH are typically variables for which
Vietnam does better and are good for education results. For instance, Vietnam has a higher
level for PRESCHOOL and for time spent by students in mathematics lessons after school
(OUTMATH). These two sets of variables together constitute the specification of the XV
and XO vectors.
Now it is an established result that the matrix decomposition reported in equation 8
29

also holds at the variable level, as the overall decomposition is nothing else but the sum
of variable level decompositions [Hlavac, 2015]. We extend this notion further to look at
aggregations by the two sets of variables - ED WEALTH and WEALTH. In the equations
below, we have m variables in the EDWEALTH set and n variables in the WEALTH set:
( ¯XA − ¯XB) ˆβR
endowments
=
m
i=1
( ¯XiV − ¯XiO)ˆβiV
ED WEALTH
+
n
i=1
( ¯XiV − ¯XiO)ˆβiV
WEALTH
(6)
¯XO(βV − βO)
coefficients
=
m
i=1
¯XiV (ˆβOV − ˆβiV )
ED WEALTH
+
n
i=1
¯XiV (ˆβOV − ˆβiV )
WEALTH
(7)
4.2 Findings from OB Decomposition
In Tables 13 through 15 we present the mathematics score findings from the OB decom-
position for Vietnam compared with each of the Dev7 countries arranged by geographic area.
We present the mean differences again between Vietnam and Dev7, then countries from Latin
America (Colombia and Peru), Eastern Europe & Central Asia (Albania), Middle East &
North Africa (Jordan and Tunisia), East Asia & Pacific’s (Indonesia and Thailand) as well as
Shanghai. The number “Sum Total ED WEALTH + WEALTH” in the bottom row indicates
the mean difference between Vietnam and each of the Dev7 countries. The top panel in each
of Tables 13 through 15 presents the ED WEALTH variables - these are variables related to
educational performance where Vietnam does better on mean values compared to the Dev7
countries. The lower panel in each of the tables presents the WEALTH variables - typically
related to a country’s income level, they are variables where the Dev7 countries do better.
The paired column of numbers for each country indicates a hypothetical counter-factual.
The ‘Endowments’ column shows how much of the mean difference arises from a difference
in the mean values, keeping the coefficients fixed at the level of Vietnam. The ‘Coefficients’
column indicates how much of the difference is due to the difference in coefficients between
Vietnam an the compared country, keeping the characteristics fixed at the mean level of the
compared country. We begin the analysis by looking at Latin American countries Colombia
and Peru in Table 13.
30

Table 13: OB Decomposition for mathematics: Sample Means and Latin America (Colombia and Peru)
Mean value Colombia Peru
Category Variable Name Dev 7 Vietnam Endowments Coefficients Endowments Coefficients
INTERCEPT 222.6017 247.8394
Students PRESCHOOL 0.7888 0.9120 0.1767 21.2995 0.8096 6.8201
LATESCHOOL 1.5131 1.1872 2.7914 -7.0084 5.3749 -6.8398
NOREPEAT 0.8085 0.9321 9.752 5.6154 5.1146 8.7662
SHRS 3.7566 3.9597 2.1314 10.2102 2.56 19.0496
Parents OUTMATH 1.8280 3.1305 6.3176 13.8406 3.6561 14.5438
PARPRESSURE 0.2665 0.3837 5.2187 5.0041 1.9524 4.8938
TIGERMOM 52.4472 62.4183 0.1958 -14.0056 -0.6088 3.4834
TEACHMOM 12.1764 38.2821 2.0458 2.2844 1.4841 3.9414
Teachers PROPCERT 0.6757 0.7961 - - - -
MATHPROFDEV 40.5068 49.0086 -0.2447 -1.0775 0.0423 -3.9528
TCH INCENTV -0.0317 0.2687 1.9205 -0.4223 2.6139 -1.3882
TCM INSPE 0.5882 0.8664 -13.3928 -0.5966 -6.3926 -11.6551
TCM OBSER 0.8015 0.9785 -9.9929 -5.2289 -1.9658 -37.8481
COMP USE 0.4345 0.6447 -0.6 3.1921 -0.3406 1.8251
STU FEEDB 0.7105 0.8419 -0.3485 -10.1593 -0.6405 5.056
Schools EXC6 MATHCOMP 0.6268 0.8032 -0.6487 -8.5158 0.0019 -10.8913
SCMATBUI -0.6322 -0.3988 -0.0363 -0.4844 -0.0379 -0.7149
SCL EXTR CL 0.6538 0.9584 -8.4506 -9.4397 -8.1854 -11.5939
SCORE PUBLIC 0.3450 0.7567 2.6639 4.0431 7.0514 2.4778
TOTAL ED WEALTH -0.5007 8.5509 12.4896 -14.0269
0% 8% 10% -11%
Students EXAPPLM 0.1111 -0.2418 0.2362 1.6552 1.7418 -0.0448
EXPUREM -0.1384 0.1587 2.9206 0.9846 -0.0688 -1.3801
LHRS 3.5990 3.2207 9.528 -44.012 14.0996 -56.6772
MHRS 3.8960 3.7878 -3.6621 14.9222 -4.8692 13.7176
Parents HISEI 40.4196 26.6023 -6.3504 7.3598 -3.7414 3.6262
MISCED 3.1193 2.1744 -2.143 -1.2957 -1.2325 -10.9724
WEALTH -1.4606 -2.1343 2.4134 17.4408 1.4103 17.2629
CULTPOS -0.1424 -0.2361 0.3183 0.327 0.4633 1.8325
HEDRES -0.7427 -1.0743 -4.8774 -7.6374 -6.0766 -3.1882
BOOK N 53.6393 50.7860 -0.0504 -5.2598 0.0089 -6.5238
Teachers TXT BOOK 0.7905 0.7855 -5.6426 -5.2097 0.629 -14.3821
CLSIZE 35.0130 42.5043 -0.5804 -46.7664 -9.4773 -36.5348
TCFOCST 0.4975 0.1402 2.7312 0.8924 1.4043 -0.7333
TCMORALE 0.0376 -0.2941 -7.7804 1.8946 -1.9897 -0.2643
TCHPARTI -0.2169 -1.6445 6.7157 1.8929 14.9263 -0.3443
Schools TOWN 0.4508 0.3101 -3.0845 -2.9438 1.4527 -6.9018
VILLAGE 0.1403 0.4584 -11.2676 -1.7407 -8.1092 -1.6368
PRIVATESCL 0.1714 0.0832 4.4454 -2.5786 10.9349 -11.4833
STU FEES 25.7233 16.6104 4.5328 -23.7404 - -
RATCMP15 0.3909 0.2216 7.6894 -12.933 3.6968 -11.777
SCHAUTON -0.2542 -1.0419 -10.9579 -0.3955 -10.3639 0.5217
EXC1 BAND 0.4710 0.1678 0.29099 -2.4563 -0.118 0.1559
TOTAL WEALTH -14.57471 -109.5998 4.7213 -125.7274
-14% -103% 4% -100%
SUM TOTAL (ED WEALTH & WEALTH) 106.4774 125.2960
100% 100%
Notes: The ’Mean values’ for Dev7 and Vietnam are taken from the whole data set and represent the same values used for Section 2 -
Endowment Differences. They are included here to reiterate the categorization of variables into WEALTH and ED WEALTH, depend-
ing on their comparative values between Dev7 and Vietnam. Underlined values represent those variables mentioned within the analysis.
31

The mean difference for Colombia is positive 106 points. Interestingly, differences on ED
WEALTH endowments make a negligible contribution and the coefficients on ED WEALTH
only account for positive 8% of the difference. The endowments on WEALTH indicate a 14%
reduction for Colombia by adopting Vietnamese endowments, and a large 103% reduction
from coefficients. The Colombia EDWEALTH coefficient column indicates some interesting
results. While PRESCHOOL (students having attended PRESCHOOL) would have had a
positive 21 point impact, note the negative 14 point impact on the variable called TIGER-
MOM (parents proactively following up with teacher regarding student’s performance) and
negative 10 point impact on STU FEEDB (teachers obtain written feedback from students).
This might indicate that some of the features of countries are related to cultural factors
that come together as a package - being a ‘Tiger Mom’ may help the child in Vietnam, but
perhaps not as much in Colombia!
A similar interpretation is possible regarding the variables TCM INSPE and
TCM OBSERVER (teachers benefit from class room observation by external inspectors and
principal/senior school staff) and SCL EXTR CL (extra classes at school). These have a
negative value in the endowment column as well as the coefficients column. This means that
if Colombian students had Vietnamese characteristics on these variables, the mean result for
Colombia would have been lower than it already is. The interpretation would be that what
is good for Vietnamese students, in a Vietnamese context as measured by PISA, may not be
good for Colombian students. However, this finding should be taken with some caution as
there are also some variables, like OUTMATH (time spent in extra classes for math outside
of school) which have positive values on the endowments and the coefficients.
Finally from Table 13 we can see that for Peru, ED WEALTH endowments make a posi-
tive 10% contribution and the coefficients make a negative 11% contribution. ED WEALTH
variables that make a positive contribution in the coefficients column include PRESCHOOL,
SHRS (hours of science instruction) and all the parent related variables. On the WEALTH
endowments, there is a positive 4% contribution and a negative 100% contribution from
coefficients.
Table 14 presents data from the next three countries, Albania from the Eastern Europe
& Central Asia region and Jordan and Tunisia from the Middle East & North Africa region.
32

Table 14: OB Decomposition for mathematics: Eastern Europe & Central Asia (Albania) and Middle East
& North Africa (Jordan and Tunisia)
Albania Jordan Tunisia
Category Variable Name Endowments Coefficients Endowments Coefficients Endowments Coefficients
INTERCEPT 100.7672 232.8262 220.3262
Students PRESCHOOL 3.2606 14.9314 2.9304 8.8395 6.1004 8.8937
LATESCHOOL 2.5351 -16.1377 4.0339 -17.9123 5.1805 -13.6441
NOREPEAT -1.1335 81.2394 -0.5863 -0.5305 11.7885 -24.2972
SHRS 8.103 18.4864 -3.4578 -1.8296 6.821 12.3146
Parents OUTMATH 8.4386 5.9606 8.4886 12.3205 1.5262 12.4973
PARPRESSURE 3.0612 6.3037 2.9216 8.3922 6.05 -2.0319
TIGERMOM 0.0071 -3.1614 -1.456 0.7928 -4.5979 -4.6769
TEACHMOM 2.4287 4.431 2.0207 0.5374 3.4577 -0.343
Teachers PROPCERT -0.8449 6.3679 - - 1.103 -1.8785
MATHPROFDEV 0.0438 -0.353 0.1404 0.0539 -0.0296 -5.3119
TCH INCENTV 3.4264 -2.4877 -0.3897 1.002 1.7686 -0.8613
TCM INSPE -5.3929 -17.0471 2.0905 4.5083 0.5072 -16.981
TCM OBSER - - 0.0738 -11.522 -0.8747 8.2309
COMP USE -0.4169 -0.3193 0.1317 -6.769 -1.3139 2.006
STU FEEDB -0.5089 7.7288 -0.4144 -4.888 -1.645 -0.1837
Schools EXC6 MATHCOMP 0.3129 -9.663 -0.8381 -2.5399 -1.3721 -0.8139
SCMATBUI 0.388 -4.8621 0.2473 -3.7528 1.4125 -13.0502
SCL EXTR CL -3.5828 -2.9217 -4.3968 -9.0748 -3.8009 -17.7227
SCORE PUBLIC 5.3549 3.4256 5.9804 4.7486 7.2212 -0.7671
TOTAL ED WEALTH 25.4804 91.9218 17.5202 -17.6237 39.3027 -58.6209
18% 64% 15% -15% 35% -52%
Students EXAPPLM 1.9364 -0.5576 2.5477 -1.9561 -0.0268 0.7122
EXPUREM -0.2203 1.3971 2.1748 2.0725 3.0066 0.953
LHRS -3.359 -34.8084 15.7577 -58.088 23.1941 -63.0774
MHRS 4.424 14.1238 -0.1466 43.9865 -3.834 24.3011
Parents HISEI - - - - -4.5879 -17.802
MISCED -5.6001 18.8463 -5.2108 -3.2033 -0.8438 9.0345
WEALTH 0.4117 -1.4204 1.5112 0.8037 0.8843 5.3532
CULTPOS 0.3533 0.5941 -0.1767 0.9315 -0.5056 0.2824
HEDRES -3.3855 -5.5085 -5.509 -3.607 -2.857 -5.5976
BOOK N 0.0053 -0.4736 -0.2911 -0.1599 0.1597 0.8503
Teachers TXT BOOK - - 3.5046 -16.7213 3.2614 -33.4627
CLSIZE -8.6779 -23.4445 -5.4853 -59.9741 -8.5485 10.7592
TCFOCST 2.9242 0.3422 1.1106 -0.8656 -2.34511 1.3381
TCMORALE -6.2933 -0.0748 -0.4259 -1.4027 3.9636 1.4813
TCHPARTI 11.3136 4.9119 0.5336 8.9057 3.4112 7.9172
Schools TOWN 5.0475 -6.7474 2.5742 -2.1225 6.7826 6.9162
VILLAGE -7.49 -0.6774 -10.417 -1.5033 -11.82 0.9214
PRIVATESCL -0.0055 -0.0083 6.1701 -12.8976 - -
STU FEES 0.671 -10.8914 - - -0.355 -19.7408
RATCMP15 2.9895 -4.1902 3.8324 -25.5703 1.8275 -7.3367
SCHAUTON -4.4263 -8.8247 1.6804 5.5624 -2.7901 -17.2538
EXC1 BAND 0.5318 -8.6246 0.0348 -2.8344 0.154 -3.1367
TOTAL WEALTH -8.8496 -66.0364 13.7697 -128.6438 8.13119 -96.5876
-6% -46% 12% -109% 7% -86%
SUM TOTAL (ED WEALTH & WEALTH) 143.2834 117.8486 112.5516
100% 100% 100%
Notes: The ’Mean values’ for Dev7 and Vietnam are taken from the whole data set and represent the same values used for Section 2 - Endow-
ment Differences. They are included here to reiterate the categorization of variables into WEALTH and ED WEALTH, depending on their
comparative values between Dev7 and Vietnam. Underlined values represent those variables mentioned within the analysis.
33

Table 14 shows that the mean difference between Albania and Vietnam for mathematics
was positive 143 points, the highest Vietnam advantage amongst all Dev7 countries. The
OB decomposition indicates that if Albanian 15 year olds had Vietnamese endowments on
ED WEALTH variables, their score would have been higher by 18% and if they had Viet-
nam’s coefficients on those variables, their score would have been higher by 64%. Looking at
the bottom panel, with Vietnam’s WEALTH endowments, Albania’s score would have been
reduced by 6% and with Vietnam’s WEALTH coefficients while retaining its own character-
istics would have lowered the score by 46%. Interpretation needs to be made with care, for
instance, a big boost would have come from the coefficient values on repetition, but this is
probably driven by the rarity of repetition in Vietnam.
With Jordan, the gap to be explained is positive 118 points. On the ED WEALTH
variables, endowment differences with Vietnam make for a positive 15% contribution and
the coefficients make for a negative 15% contribution. Vietnamese endowments on ED
WEALTH would make a reasonable contribution for Jordanian students, but the coefficients
would pull in the opposite direction. Six variables contribute mainly to this negative direction
- LATESCHOOL (number of times arriving late in the schoolday), TCM OBSER (teacher
classroom observation by principal or senior school staff), STU FEEDB (written feedback
from students for teacher), SCL EXTR CL, EXC6 MATHCOMP (mathematics competition
as extra-curricular activity) and SCMATBUI (index of quality of school infrastructure). On
the WEALTH side, there is a positive 12% contribution of endowments and a negative 109%
contribution of coefficients. The variables which contribute to this anomalous result include
PRIVATESCL, LHRS (hours of language instruction) and RATCMP15 (available computers
for 15 year olds).
The mean difference for Tunisia is positive 113 points. Tunisia indicates the highest
positive value of ED WEALTH endowments amongst all countries (35%) and also the highest
negative value on the coefficients (-52%). The results for WEALTH for Tunisia indicate a
positive 7% contribution on endowments and a negative 86% contribution on coefficients.
The constituent variables have made their appearance in the previous commentary - for
example, the contributions to the negative value on ED WEALTH coefficients for Tunisia
come from LATESCHOOL (-13.6), NOREPEAT (-24.3), TIGERMOM (-4.6), SCMATBUI
(-13.05), and SCL EXTR CL (-17.72).
Next, we turn to Table 15 which includes the remaining set of Dev7 countries from the
East Asia & Pacific’s region - Thailand and Indonesia as well as the additional case of
Shanghai, which has much better results even than Vietnam, and is included as an East
Asian counterpoint to the Dev7 countries.
34

Table 15: OB Decomposition for mathematics: East Asia & Pacific’s (Indonesia and Thailand) and Shang-
hai
Indonesia Thailand Shanghai
Category Variable Name Endowments Coefficients Endowments Coefficients Endowments Coefficients
INTERCEPT 181.3475 197.3024 -88.5547
Students PRESCHOOL 6.2309 6.0373 -1.3537 -17.271 1.0135 3.3683
LATESCHOOL 1.3545 0.1656 2.4103 -6.5245 -0.7333 -9.4329
NOREPEAT 3.0327 15.4797 -0.8545 18.0457 -2.4686 18.736
SHRS 3.6461 3.2498 -4.6096 -1.3158 2.7618 11.5291
Parents OUTMATH 9.9458 3.9547 6.7928 12.3212 1.4415 -19.6668
PARPRESSURE 0.2358 7.19 -1.4518 4.2812 -3.295 -2.5603
TIGERMOM -1.4622 -5.0425 -0.388 -2.3087 1.7009 -8.999
TEACHMOM 2.1566 1.7254 2.753 0.1281 -4.4387 3.3083
Teachers PROPCERT 1.0728 7.8387 -0.4727 31.4282 8.3337 43.4759
MATHPROFDEV -0.0778 -4.2427 0.2289 0.5997 0.0199 0.5484
TCH INCENTV 0.2442 0.6929 -0.3042 2.7973 0.3293 0.0993
TCM INSPE -1.7713 -7.5635 -6.9777 -9.7581 0.4269 23.093
TCM OBSER -0.0742 11.6516 -0.0414 35.1591 -0.0214 -65.8268
COMP USE -0.6291 1.714 0.0932 3.2715 2.1571 -3.3878
STU FEEDB 0.0796 7.9599 -0.0804 -3.4671 0.9525 15.1379
Schools EXC6 MATHCOMP -0.3133 1.5705 -0.6327 -15.9607 -7.5785 46.1336
SCMATBUI 0.0048 -1.1337 0.4918 -0.3168 1.369 -0.9102
SCL EXTR CL -2.9043 -20.2148 -0.299 -31.4213 -6.1388 29.4929
SCORE PUBLIC 5.7895 1.2721 0.1084 -1.5941 -13.9127 6.2176
TOTAL ED WEALTH 26.5611 32.305 -4.5873 18.0939 -18.0809 90.3565
19% 23% -6% 25% -22% 111%
Students EXAPPLM 1.2618 -0.0988 2.6597 -0.6224 -1.635 0.0304
EXPUREM 2.1991 0.1956 1.1331 -0.244 -0.0536 -1.354
LHRS -2.0941 -29.3342 -10.6968 -28.5366 -11.4368 1.8702
MHRS 1.0459 14.6798 0.4215 -18.4475 4.4884 7.1219
Parents HISEI -1.7446 -0.6415 -3.7308 3.9065 8.6256 0.6227
MISCED -0.4967 6.245 -1.5344 -3.2852 2.0466 0.206
WEALTH -0.5319 -1.6968 1.9975 4.6253 -6.7778 6.9447
CULTPOS -0.3684 0.7266 0.2273 -0.0479 2.4812 -0.9197
HEDRES 2.5025 -8.659 -6.2788 -2.0394 7.1149 2.2072
BOOK N -0.062 1.7571 -0.3221 -4.9515 5.1709 4.2595
Teachers TXT BOOK 1.2766 -17.5263 2.749 -10.9737 -2.4418 0.6483
CLSIZE -4.7921 -13.5878 -3.667 -20.3239 0.4045 23.7015
TCFOCST 4.5553 -4.0316 4.7078 -9.8512 0.0868 0.7556
TCMORALE -9.9416 1.028 -4.4468 1.0381 0.5319 2.9116
TCHPARTI 18.5498 -10.6116 27.6836 -19.9891 -0.8565 -11.9873
Schools TOWN 5.1411 -1.5651 3.5728 -3.3756 - -
VILLAGE -7.3432 -1.496 -9.3596 -2.0706 - -
PRIVATESCL 2.0065 0.0116 0.1877 1.3994 1.9492 3.1875
STU FEES 11.4805 -29.6824 0.2682 -8.9641 -0.1311 3.278
RATCMP15 -1.56 -11.0681 7.5765 -21.4644 -3.1063 3.4899
SCHAUTON -17.2045 8.6425 -21.8484 10.7935 -7.8405 20.4172
EXC1 BAND 0.4597 -7.9357 0.6927 3.9018 24.6725 6.7965
TOTAL WEALTH 4.3397 -104.6487 -8.0073 -129.5225 23.2931 74.1877
3% -75% -11% -177% 29% 91%
SUM TOTAL (ED WEALTH & WEALTH) 139.9046 73.2792 81.2017
100% 100% 100%
Notes: The ’Mean values’ for Dev7 and Vietnam are taken from the whole data set and represent the same values used for Section 2 - Endow-
ment Differences. They are included here to reiterate the categorization of variables into WEALTH and ED WEALTH, depending on their
comparative values between Dev7 and Vietnam. Underlined values represent those variables mentioned within the analysis.
35

Table 15 indicates that in the case of Indonesia, the gap to be explained is positive 140
points. Endowments and coefficients on ED WEALTH account for positive 19% and positive
23% of the gap. The ED WEALTH variables explain more as was the case for Colombia.
When we look at the WEALTH related variables, we see that the endowments of Indonesia
would not have made such a big impact, pointing to the fact that Indonesia is closest to
Vietnam amongst the Dev7 countries on per capita income. Of a similar magnitude like
Colombia, we can see that the WEALTH coefficients of Vietnam would set back Indonesian
students by negative 75%.
Thailand is the country with the lowest difference in mathematics score, only positive 73
points behind Vietnam. The OB decomposition for Thailand indicates a negative 6% con-
tribution of endowments on EDWEALTH and a positive 25% contribution on coefficients.
On the WEALTH set of variables, the contribution for Thailand was negative 11% on en-
dowments and negative 177 % on coefficients. The results from Shanghai, added to the mix
as a counterpoint to Dev7 countries, do not seem to provide much additional insight. The
biggest exlanation of difference between Vietnam and Shanghai would the coefficients on ED
WEALTH and WEALTH, with 111% and 91% respectively. 11
Overall, the OB decompositions support the previous findings. In five of the seven Dev7
countries, the ED WEALTH variables show a positive contribution on endowments - meaning
that if the other countries have had Vietnam’s endowments on ED WEALTH variables, their
performance would have been better. On the coefficients side of ED WEALTH, we see a
different picture - the contributions range from +64% for Albania to -52% for Tunisia, with
other countries ranged in between. The two Asian countries (Indonesia and Thailand) have
similar contributions of 23% and 25%. The predictable WEALTH set of decompositions is of
less interest to us as in most cases there are small effects on endowments and large negative
effects on coefficients.
With cross-section data in a non-experimental context, it is very difficult to make defini-
tive conclusions, and only tentative ones can be made that hint at some answers. The
findings on Dev7 countries indicate that it is possible that a number of advantages that
Vietnam enjoys, depicted in ED WEALTH, can only function effectively in combination.
One way to consider this is through a cultural lens - meaning that there is something spe-
cific to Vietnamese culture, that enables Vietnam to benefit from hard working students
and teachers, with the guidance of committed and involved parents, even in cities and small
towns.
11
For all other countries we used the respective country as the basis; for Shanghai, Vietnam is the base
country in terms of equation (5).
36

5 Conclusion
This paper has sought to focus attention and find insights regarding a most remarkable
PISA 2012 result - the superlative performance of Vietnam, a country with the lowest per
capita income amongst all PISA participants. Vietnam, with a mean PISA math score of
511 is not one of the very top performers. However, when compared with other lower middle
income countries that took part in PISA 2012, Vietnam is a clear outlier. The following
three concluding points can be made as a result of the analysis presented in this paper.
1. Half the gap can be explained: Even though the PISA dataset is rich and covers
many aspects related to the achievement of student scores with international standardization
of measures, with all the available variables, we could explain at best about 50% of the
performance gap of Vietnam. The PISA 2015 application will be especially interesting to
study as it will provide another important data point and enable a trend analysis to be
conducted.
2. Cultural factors are likely very important: A combination of three sets of factors
appear to be the most potent explanation for Vietnam’s performance: First, Vietnamese
students work harder - we see they have less instances of skipped classes and being late for
school, spend about the same time or more learning in school and substantial extra time
studying after school. While at school, Vietnamese students are more disciplined and focused
on their studies. Second, Vietnamese teachers appear to benefit from a closer supervision of
their work by the school principal and others, and there may be a stronger harmony between
the hard working students and their teachers. Third, parents may have an important role
to play, by taking an active part in combining high expectations of their children, following
up with their children’s teachers and contributing at school.
3. Resources do appear to matter: When we compare PISA performances across
the range from lower income non-OECD countries to the high income OECD countries,
we find a clear positive trend. Vietnam has so far been the only PISA outlier, with a
performance on par with much wealthier countries, and in fact one of the top performing
countries in Science. The analysis indicates that Vietnam may be reaping the benefits of
policies regarding investments in education - the most important factor probably being the
higher level of access to pre-school. A second factor is the investment in school infrastructure,
especially in cities and small towns.
The unique combination of focused educational investments beyond its income level and
a cultural heritage that has positive behavioral implications for students appear to be part
of the story behind Vietnam’s educational success.
37

Unraveling a Secret: Vietnam's Outstanding Performance on the PISA test

Unraveling a Secret: Vietnam's Outstanding Performance on the PISA test

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Unraveling a Secret: Vietnam's Outstanding Performance on the PISA test